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Population Coding of Generative Neuronal Cells for Collaborative Decision Making in UAV-Based SLAM Operations

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Abstract

This study presents a modeling method for control system of multi-coupled nonlinear cyber physical system with the help of second-order dynamics of communicating membrane system of neuronal population based on a population coding algorithm. Here, the communicating membrane system is modeled based on the theory of fractional differential equations. Here, the challenge relies on the development of a collaborative multi-coupled system with self-learning attributes, which is essential for finding a collaborative workspace between data streams thereby giving increase in high-dimensional decision space. Therefore, to curb this issue, a gradient-based approach is appropriate with co-simulation features to enable several distributed units to be controlled and collaborative for decision-making applications. Here, at every interval of the sampling time, all the subsystems are optimally synchronized in P population system by multiset-rewriting rules including the effect of symport/antiport systems. This remains a vital problem, to classify and mathematically patch the borderline interjection between universality and non-universality of continuity of distributed control system. The presented study discusses the experimental proof of the algorithmic framework and model for SLAM operation. The consensus architecture in this domain will enable the development of evolving architecture of a nonlinear cyber physical system. The closed-loop stability and the recursive feasibility of the evolved architecture are also studied.

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Authors and Affiliations

  1. School of Computing Science and Engineering, VIT University, Chennai, 600127, India

    Ankush Rai & R. Jagadeesh Kannan

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  1. Ankush Rai
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  2. R. Jagadeesh Kannan
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Correspondence to Ankush Rai.

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Rai, A., Kannan, R.J. Population Coding of Generative Neuronal Cells for Collaborative Decision Making in UAV-Based SLAM Operations. J Indian Soc Remote Sens 49, 499–505 (2021). https://doi.org/10.1007/s12524-020-01245-x

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  • DOI: https://doi.org/10.1007/s12524-020-01245-x

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